A Boosting Approach to Multiple Instance Learning. - Peter Auer; Ronald Ortner (ECML, 2004)

On the Combinatorial Structure of Arrangements of Oriented Pseudocircles. - Johann Linhart; Ronald Ortner (Electron. J. Comb., 2004)

Combinations and Mixtures of Optimal Policies in Unichain Markov Decision Processes are Optimal - Ronald Ortner (CoRR, 2005)

Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning. - Peter Auer; Ronald Ortner (NIPS, 2006)

Pseudometrics for State Aggregation in Average Reward Markov Decision Processes. - Ronald Ortner (ALT, 2007)

Improved Rates for the Stochastic Continuum-Armed Bandit Problem. - Peter Auer; Ronald Ortner; Csaba Szepesvári (COLT, 2007)

A new PAC bound for intersection-closed concept classes. - Peter Auer; Ronald Ortner (Mach. Learn., 2007)

Linear dependence of stationary distributions in ergodic Markov decision processes. - Ronald Ortner (Oper. Res. Lett., 2007)

Embeddability of arrangements of pseudocircles into the sphere. - Ronald Ortner (Eur. J. Comb., 2008)

Optimism in the Face of Uncertainty Should be Refutable. - Ronald Ortner (Minds Mach., 2008)

Exploiting Similarity Information in Reinforcement Learning - Similarity Models for Multi-Armed Bandits and MDPs. - Ronald Ortner (ICAART (1), 2010)

Near-optimal Regret Bounds for Reinforcement Learning. - Thomas Jaksch; Ronald Ortner; Peter Auer (J. Mach. Learn. Res., 2010)

UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem. - Peter Auer; Ronald Ortner (Period. Math. Hung., 2010)

Online regret bounds for Markov decision processes with deterministic transitions. - Ronald Ortner (Theor. Comput. Sci., 2010)

PAC-Bayesian Analysis of Contextual Bandits. - Yevgeny Seldin; Peter Auer; François Laviolette; John Shawe-Taylor; Ronald Ortner (NIPS, 2011)

Mechanizing Induction. - Ronald Ortner; Hannes Leitgeb (Inductive Logic, 2011)

Online Regret Bounds for Undiscounted Continuous Reinforcement Learning. - Ronald Ortner; Daniil Ryabko (NIPS, 2012)

Competing with an Infinite Set of Models in Reinforcement Learning. - Phuong Nguyen; Odalric-Ambrym Maillard; Daniil Ryabko; Ronald Ortner (AISTATS, 2013)

Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning. - Odalric-Ambrym Maillard; Phuong Nguyen; Ronald Ortner; Daniil Ryabko (ICML (1), 2013)

Adaptive aggregation for reinforcement learning in average reward Markov decision processes. - Ronald Ortner (Ann. Oper. Res., 2013)

Selecting Near-Optimal Approximate State Representations in Reinforcement Learning. - Ronald Ortner; Odalric-Ambrym Maillard; Daniil Ryabko (ALT, 2014)

Regret bounds for restless Markov bandits. - Ronald Ortner; Daniil Ryabko; Peter Auer; Rémi Munos (Theor. Comput. Sci., 2014)

Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning. - K. Lakshmanan 002; Ronald Ortner; Daniil Ryabko (ICML, 2015)

Forcing subarrangements in complete arrangements of pseudocircles. - Ronald Ortner (J. Comput. Geom., 2015)

Pareto Front Identification from Stochastic Bandit Feedback. - Peter Auer; Chao-Kai Chiang; Ronald Ortner; Madalina M. Drugan (AISTATS, 2016)

Improved Learning Complexity in Combinatorial Pure Exploration Bandits. - Victor Gabillon; Alessandro Lazaric; Mohammad Ghavamzadeh; Ronald Ortner; Peter L. Bartlett (AISTATS, 2016)

Optimal Behavior is Easier to Learn than the Truth. - Ronald Ortner (Minds Mach., 2016)

Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning. - Ronan Fruit; Matteo Pirotta; Alessandro Lazaric; Ronald Ortner (ICML, 2018)

A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions. - Pratik Gajane; Ronald Ortner; Peter Auer (CoRR, 2018)

Guest Editors' Foreword. - Ronald Ortner; Hans Ulrich Simon (Theor. Comput. Sci., 2018)

Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information. - Peter Auer; Yifang Chen; Pratik Gajane; Chung-Wei Lee; Haipeng Luo; Ronald Ortner; Chen-Yu Wei (COLT, 2019)

Adaptively Tracking the Best Bandit Arm with an Unknown Number of Distribution Changes. - Peter Auer; Pratik Gajane; Ronald Ortner (COLT, 2019)

Regret Bounds for Learning State Representations in Reinforcement Learning. - Ronald Ortner; Matteo Pirotta; Alessandro Lazaric; Ronan Fruit; Odalric-Ambrym Maillard (NeurIPS, 2019)

Variational Regret Bounds for Reinforcement Learning. - Ronald Ortner; Pratik Gajane; Peter Auer (UAI, 2019)

Autonomous exploration for navigating in non-stationary CMPs. - Pratik Gajane; Ronald Ortner; Peter Auer; Csaba Szepesvári (CoRR, 2019)

Regret Bounds for Reinforcement Learning via Markov Chain Concentration. - Ronald Ortner (J. Artif. Intell. Res., 2020)

A new heuristic and an exact approach for a production planning problem. - Peter Auer; György Dósa; Tibor Dulai; Armin Fügenschuh; Peggy Näser; Ronald Ortner; Agnes Werner-Stark (Central Eur. J. Oper. Res., 2021)

Transfer in Reinforcement Learning via Regret Bounds for Learning Agents. - Adrienne Tuynman; Ronald Ortner (CoRR, 2022)

Predicting Packaging Sizes Using Machine Learning. - Michael Heininger; Ronald Ortner (Oper. Res. Forum, 2022)

Decision Making Under Uncertainty and Reinforcement Learning - Theory and Algorithms - Christos Dimitrakakis; Ronald Ortner (Springer, 2022)

Autonomous Exploration for Navigating in MDPs Using Blackbox RL Algorithms. - Pratik Gajane; Peter Auer; Ronald Ortner (IJCAI, 2023)

Regret Bounds for Satisficing in Multi-Armed Bandit Problems. - Thomas Michel; Hossein Hajiabolhassan; Ronald Ortner (Trans. Mach. Learn. Res., 2023)